Posterior predictive checks for the winning DDM_s and alternative model formulations for the seq. RL task data for the placebo and tyrosine condition. by David Mathar (9707947)

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S6 Fig - by David Mathar (9707947)

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Effects on participants’ (N = 28) probability to choose the same option in S1 as in the previous trial from a generalized mixed effects regression (glmer) analysis (rew = reward; t... by David Mathar (9707947)

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Fig 8 - by David Mathar (9707947)

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Tyrosine related changes in group-level means of the DDM_s for the temporal discounting task data. by David Mathar (9707947)

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S4 Fig - by David Mathar (9707947)

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A flowchart describes the stepwise analysis of missense SNPs in the human PC and illustrates the computational tools used in the study. by Mahvash Farajzadeh-Dehkordi (17468232)

A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease by Robert J. Ellis (668685)

“…</p><p>Methods</p><p>The accuracy of smartphone-based gait analysis (utilizing the smartphone’s built-in tri-axial accelerometer and gyroscope to calculate successive step times and step lengths) was validated against two heel contact–based measurement devices: heel-mounted footswitch sensors (to capture step times) and an instrumented pressure sensor mat (to capture step lengths). 12 PD patients and 12 age-matched healthy controls walked along a 26-m path during self-paced and metronome-cued conditions, with all three devices recording simultaneously.…”

Influence of R–NHC Coupling on the Outcome of R–X Oxidative Addition to Pd/NHC Complexes (R = Me, Ph, Vinyl, Ethynyl) by Evgeniy G. Gordeev (1724383)

“…Oxidative addition of organic halides (R–X) to (NHC)­Pd⁰L complexes is involved in numerous metal-catalyzed reactions, and this step is expected to afford (NHC)­Pd^II(R)­(X)­L intermediate complexes. …”

HF prediction model parameters. by Miho Iwasaki (22553084)

“…Herein, 28 healthy individuals were repeatedly moved between two temperature environments, and their psychological/physiological responses to temperature differences in the environment were recorded [T_26-26 (control), T_26-31 (5 °C step), T_26-36 (10 °C step), and T_21-36 (15 °C step)]. …”

Skin temperature model parameters. by Miho Iwasaki (22553084)

“…Herein, 28 healthy individuals were repeatedly moved between two temperature environments, and their psychological/physiological responses to temperature differences in the environment were recorded [T_26-26 (control), T_26-31 (5 °C step), T_26-36 (10 °C step), and T_21-36 (15 °C step)]. …”

Raw data of each parameter. by Miho Iwasaki (22553084)

“…Herein, 28 healthy individuals were repeatedly moved between two temperature environments, and their psychological/physiological responses to temperature differences in the environment were recorded [T_26-26 (control), T_26-31 (5 °C step), T_26-36 (10 °C step), and T_21-36 (15 °C step)]. …”

Feeling of physical fatigue model parameters. by Miho Iwasaki (22553084)

“…Herein, 28 healthy individuals were repeatedly moved between two temperature environments, and their psychological/physiological responses to temperature differences in the environment were recorded [T_26-26 (control), T_26-31 (5 °C step), T_26-36 (10 °C step), and T_21-36 (15 °C step)]. …”

Skin temperature prediction model parameters. by Miho Iwasaki (22553084)

“…Herein, 28 healthy individuals were repeatedly moved between two temperature environments, and their psychological/physiological responses to temperature differences in the environment were recorded [T_26-26 (control), T_26-31 (5 °C step), T_26-36 (10 °C step), and T_21-36 (15 °C step)]. …”

HF model parameters. by Miho Iwasaki (22553084)

“…Herein, 28 healthy individuals were repeatedly moved between two temperature environments, and their psychological/physiological responses to temperature differences in the environment were recorded [T_26-26 (control), T_26-31 (5 °C step), T_26-36 (10 °C step), and T_21-36 (15 °C step)]. …”

HR model parameters. by Miho Iwasaki (22553084)

“…Herein, 28 healthy individuals were repeatedly moved between two temperature environments, and their psychological/physiological responses to temperature differences in the environment were recorded [T_26-26 (control), T_26-31 (5 °C step), T_26-36 (10 °C step), and T_21-36 (15 °C step)]. …”

LF/HF model parameters. by Miho Iwasaki (22553084)

“…Herein, 28 healthy individuals were repeatedly moved between two temperature environments, and their psychological/physiological responses to temperature differences in the environment were recorded [T_26-26 (control), T_26-31 (5 °C step), T_26-36 (10 °C step), and T_21-36 (15 °C step)]. …”

LF/HF prediction model parameters. by Miho Iwasaki (22553084)

“…Herein, 28 healthy individuals were repeatedly moved between two temperature environments, and their psychological/physiological responses to temperature differences in the environment were recorded [T_26-26 (control), T_26-31 (5 °C step), T_26-36 (10 °C step), and T_21-36 (15 °C step)]. …”

HR prediction model parameters. by Miho Iwasaki (22553084)

“…Herein, 28 healthy individuals were repeatedly moved between two temperature environments, and their psychological/physiological responses to temperature differences in the environment were recorded [T_26-26 (control), T_26-31 (5 °C step), T_26-36 (10 °C step), and T_21-36 (15 °C step)]. …”

Forecasting severe grape downy mildew attacks using machine learning by Mathilde Chen (8566473)

“…One solution would be to identify vineyards that are likely to be heavily attacked in spring and then apply fungicidal treatments only to these situations. In this perspective, we use here a dataset including 9 years of GDM observations to develop and compare several generalized linear models and machine learning algorithms predicting the probability of high incidence and severity in the Bordeaux region. …”